Quantifying the Effects of Photoperiod, Temperature and Daily Irradiance on Flowering Time of Soybean Isolines
Bibliographic record
Abstract
Soybean isolines with different combinations of photoperiod sensitivity alleles were planted in a greenhouse at different times during the year resulting in natural variation in daily incident irradiance and duration. The time from planting to first flower were observed. Mathematical models, using additive and multiplicative modes, were developed to quantify the effect of photoperiod, temperature, photoperiod-temperature interactions, rate of photoperiod change, and daily solar irradiance on flowering time. Observed flowering times correlated with predicted times (R² = 0.92, Standard Error of the Estimate (SSE) = 2.84 d, multiplicative mode; R² = 0.91, SSE = 2.88 d, additive mode). The addition of a rate of photoperiod change function and an irradiance function to the temperature and photoperiod functions improved the accuracy of flowering time prediction. The addition of a modified photoperiod function, which allowed for photoperiod sensitivity at shorter photoperiods, improved prediction of flowering time. Both increasing and decreasing rate of photoperiod change, as well as low levels of daily irradiance delayed flowering in soybean. The complete model, which included terms for the rate of photoperiod change, photoperiod, temperature and irradiance, predicted time to first flower in soybean across a range of environmental conditions with an SEE of 3.6 days when tested with independent data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".